AI Agents in Microsoft Teams vs Slack: Setup, Permissions, and Daily Use

Same agents, two different install paths

If your team runs on Slack, adding AI agents is close to nothing: someone with workspace permissions installs the app, and anyone can @mention it in a channel or DM it directly a minute later. If your team runs on Teams, the same outcome takes a different route, and it's worth understanding before you start rather than after IT asks why an unapproved app is requesting permissions.

This isn't a ranking of which platform is better for AI agents. It's the practical decision: what each setup actually involves, and where the day-to-day experience diverges once the agents are running.

How to create an AI agent in Microsoft Teams

Teams treats third-party apps, including AI agents, as something an administrator provisions rather than something an individual installs. The rough sequence:

1. 1
   An admin approves the app in the Teams Admin Center. Unlike Slack, a regular user typically can't add an app to a shared workspace on their own — Teams app permission policies gate this at the org level by default.
2. 2
   The app gets added to a team or channel. Once approved, it's added the way any Teams app is added — via the app store inside Teams, scoped to specific teams or channels rather than the whole org.
3. 3
   Users @mention or DM the agent. From here it behaves like Slack: type @ plus the agent's name in a channel, or open a direct message with it.
4. 4
   Permissions get scoped per team. A Teams admin can restrict which teams see which agents, which matters for larger orgs running finance and marketing on separate boundaries.

The extra step is the admin approval, and it's not a formality — Microsoft's default tenant policies often block third-party app installation until someone with admin rights turns it on. If you're hitting a wall trying to add an agent to Teams and nothing seems to happen, that's usually why.

How to add an AI agent to Slack

Slack's model assumes individual App Directory access is fine by default, though workspace owners can lock this down too:

1. 1
   Someone with app-install permission adds it from the Slack App Directory or an install link, which is usually one click and an OAuth approval screen.
2. 2
   The agent is available workspace-wide immediately, or scoped to specific channels if the admin restricts it during setup.
3. 3
   Users @mention it in a channel or DM it. No separate provisioning step per person.

For a small or mid-size team, this is the whole process — no ticket to IT, no waiting on a Teams admin's schedule. If you want to understand what separates a working Slack agent from a chat window, the answer comes down to whether it acts outside the conversation, remembers what happened last time, and can show its work.

Where the two actually behave differently once agents are running

• Admin visibility: Both platforms let an admin see which apps are installed, but Teams' governance model is built for larger, more locked-down orgs — permission policies, per-team scoping, conditional access — while Slack's is closer to install-and-go. If your organization already has strict Teams governance, agents inherit it, which can be exactly what a security team wants.
• Threading and context: Slack's flat channel structure means an agent @mentioned in a channel sees that channel's conversation. Teams nests channels inside teams inside an org hierarchy, so where an agent is added determines what context it has visibility into — worth mapping out before a company-wide rollout in Teams specifically, so an agent doesn't end up added to a channel with none of the context it needs.
• Notification behavior: Users report Teams notifications for app mentions can lag or get suppressed depending on notification settings and focus-time features; Slack's notification model for app mentions is generally more immediate. Test this with whichever platform you're deploying to rather than assuming parity.
• Adoption friction: Because Slack installation doesn't require an admin ticket, teams tend to try an agent faster and abandon it faster if it doesn't deliver. Teams' heavier install process means fewer people try it casually, but the ones who do have usually gotten sign-off from someone who intends to keep it.

Which platform to put your agents in

If you're choosing where to run agents and both platforms are live in your organization, the practical answer is: put them where the work already happens. A sales team that lives in Slack channels with prospects and partners should get agents in Slack. An enterprise IT or ops team standardized on Teams because of Microsoft 365 governance should get agents in Teams — fighting that standardization to force Slack in usually costs more in approval friction than it saves in agent installation friction.

Appy runs in both. The same named specialist team — Piper for outreach drafts, Sage for financial analysis, Scout for competitive research — are reachable by @mention or DM in whichever platform your team already uses, and nothing about the agent's memory or output changes based on where the conversation happens. The install step is the only place the platforms genuinely diverge; the work looks the same once you're through it.

What doesn't change between platforms

If you're still orienting to the product itself, here's what Appy.AI is in one page — who the specialists are, how the team installs, and what separates it from a general chatbot.

Regardless of where an agent runs, the same honest limits apply: agents draft outreach and reports rather than sending them or executing financial transactions on their own, and the agent is only as useful as the systems it's actually connected to. A Teams agent with nothing plugged in behaves exactly like a Slack agent with nothing plugged in — competently, and about nothing.

If you've already read a general overview of AI agents in Microsoft Teams, this is the piece that picks up where that one stops: not what an agent in Teams can do in the abstract, but what getting one running actually takes, and what changes once it is.

Back to the blog for more on how AI agents work in practice. View all articles